Familiarize yourself with advanced deep learning architectures
Explore newer topics, such as handling hidden bias in data and algorithm explainability
Get to grips with different programming algorithms and choose the right data structures for their optimal implementation

Description

The ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works. You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them. Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use. You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT. Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.

Who is this book for?

This computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code. Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Python programming experience is a must, knowledge of data science will be helpful but not necessary.

What you will learn

Design algorithms for solving complex problems
Become familiar with neural networks and deep learning techniques
Explore existing data structures and algorithms found in Python libraries
Implement graph algorithms for fraud detection using network analysis
Delve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examples
Create a recommendation engine that suggests relevant movies to subscribers
Grasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMs

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corona79 Oct 31, 2024

This is a big book, so there should be something of interest to anyone interested in algorithms in general, or as applicable to a particular set of problems. If a reader is not knowledgeable in this area, he or she will probably be quite impressed by the breadth of topics this text covers. I wish I could say the same, but as someone who had read more than a few books on topics that are [allegedly] covered here, I can't concur.In chapter 3, sorting algorithms are presented. Quicksort is only mentioned in passing. Given its well-deserved real-world popularity, this is unforgivable. Also missing is any discussion of hybrid and non-comparison sorting methods. The author neglected to include a "performance analysis" of merge sort.I muddled through a few more chapters. There is undoubtedly some useful material. The problem is that there are too many instances of ambiguous text or just flat-out wrong assertions.I have reviewed a number of books here. This is the first time I did not completely read the text. Chapter 7 is about supervised learning algorithms. On page 195, in discussing binary classification, we are correctly told y' represents the predicted label. Therefore, it has only two possible values - 0 or 1. Just a couple of paragraphs later, an equation is presented with y' = P(...). Yes, it is now supposedly a probability, which of course is a real number in the range [0 .. 1]. That straw was more than sufficient to break the camel's back. I have to wonder how much the 3, yes THREE, reviewers were paid.

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Kyle Oct 16, 2024

I stopped reading after finding multiple objectively incorrect statements, such as O(logn) being better than O(1) and saying that the best case for a bubble search is O(1)... How can I trust on more advanced algorithms? I hope its just typos...

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anon Aug 31, 2024

Lots of editing issues where code is not there when the text references it. Also many times in depth analysis of easy algos but then skips right by ones that are actually used(example: pages on bubble sort and nothing on quick sort, and merge sort it doesn't even give big o). Almost reads as if AI written since it it doesn't have consistent sections throughout a single chapter. The best part of the book is it at least brings different concepts to readers attention even if they are glossed over.

RSR May 02, 2024

It is just too early for a review. The logic sequence of its content looks good. Initially, I will give it 3 stars and through the reading I will increase or decrease the stars.

Sandra Patricia Bermudez Kasperczak Feb 22, 2024

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50 Algorithms Every Programmer Should Know: Tackle computer science challenges with classic to modern algorithms in machine learning, software design, data systems, and cryptography , Second Edition

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